We note that recent work, such as Swin [1]} and MViT [2]}, address the single multi-scale feature map problem by modifying the core ViT architecture (in pre-training) so it is inherently multi-scale. This is an important direction, but it also complicates the simple ViT design and may impede the exploration of new unsupervised learning directions, such as methods that sparsely process unmasked patches [3]}. Therefore, we focus on external additions to ViTs that allow them to integrate into multi-scale detection systems. We also note that Beal  [4]} integrate standard ViT models with Faster R-CNN [5]}, but report substantially lower AP\(^\text{box}\)  compared to our results (\(>\) 10 points lower), which suggests that our design is highly effective.
